{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa6992cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scipy==1.13.1\n",
      "  Obtaining dependency information for scipy==1.13.1 from https://files.pythonhosted.org/packages/4a/48/4513a1a5623a23e95f94abd675ed91cfb19989c58e9f6f7d03990f6caf3d/scipy-1.13.1-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading scipy-1.13.1-cp311-cp311-win_amd64.whl.metadata (60 kB)\n",
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      "Requirement already satisfied: numpy<2.3,>=1.22.4 in c:\\users\\31873\\anaconda3\\lib\\site-packages (from scipy==1.13.1) (1.26.4)\n",
      "Downloading scipy-1.13.1-cp311-cp311-win_amd64.whl (46.2 MB)\n",
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      "   ---------------------------------------  46.1/46.2 MB 562.0 kB/s eta 0:00:01\n",
      "   ---------------------------------------  46.2/46.2 MB 563.9 kB/s eta 0:00:01\n",
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      "   ---------------------------------------  46.2/46.2 MB 563.9 kB/s eta 0:00:01\n",
      "   ---------------------------------------- 46.2/46.2 MB 558.7 kB/s eta 0:00:00\n",
      "Installing collected packages: scipy\n",
      "Successfully installed scipy-1.13.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution ~cipy (C:\\Users\\31873\\anaconda3\\Lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution ~cipy (C:\\Users\\31873\\anaconda3\\Lib\\site-packages)\n",
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gensim 4.3.0 requires FuzzyTM>=0.4.0, which is not installed.\n"
     ]
    }
   ],
   "source": [
    "pip install scipy==1.13.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2f2480f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scipy版本: 1.13.1\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "print(\"Scipy版本:\", scipy.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41fc7bfd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting gensim==4.3.3Note: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "  Obtaining dependency information for gensim==4.3.3 from https://files.pythonhosted.org/packages/f5/57/f2e6568dbf464a4b270954e5fa3dee4a4054d163a41c0e7bf0a34eb40f0f/gensim-4.3.3-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading gensim-4.3.3-cp311-cp311-win_amd64.whl.metadata (8.2 kB)\n",
      "Requirement already satisfied: numpy<2.0,>=1.18.5 in c:\\users\\31873\\anaconda3\\lib\\site-packages (from gensim==4.3.3) (1.26.4)\n",
      "Requirement already satisfied: scipy<1.14.0,>=1.7.0 in c:\\users\\31873\\anaconda3\\lib\\site-packages (from gensim==4.3.3) (1.13.1)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in c:\\users\\31873\\anaconda3\\lib\\site-packages (from gensim==4.3.3) (5.2.1)\n",
      "Downloading gensim-4.3.3-cp311-cp311-win_amd64.whl (24.0 MB)\n",
      "   ---------------------------------------- 0.0/24.0 MB ? eta -:--:--\n",
      "   ---------------------------------------- 0.0/24.0 MB 991.0 kB/s eta 0:00:25\n",
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      "   - -------------------------------------- 0.8/24.0 MB 2.8 MB/s eta 0:00:09\n",
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      "   ------------------ --------------------- 11.1/24.0 MB 13.4 MB/s eta 0:00:01\n",
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      "   ----------------------- ---------------- 14.1/24.0 MB 15.6 MB/s eta 0:00:01\n",
      "   ------------------------ --------------- 14.9/24.0 MB 17.2 MB/s eta 0:00:01\n",
      "   -------------------------- ------------- 15.7/24.0 MB 19.8 MB/s eta 0:00:01\n",
      "   -------------------------- ------------- 16.1/24.0 MB 18.2 MB/s eta 0:00:01\n",
      "   ---------------------------- ----------- 16.9/24.0 MB 16.8 MB/s eta 0:00:01\n",
      "   ---------------------------- ----------- 16.9/24.0 MB 16.8 MB/s eta 0:00:01\n",
      "   ---------------------------- ----------- 16.9/24.0 MB 16.8 MB/s eta 0:00:01\n",
      "   ------------------------------ --------- 18.6/24.0 MB 16.0 MB/s eta 0:00:01\n",
      "   ------------------------------- -------- 18.8/24.0 MB 15.6 MB/s eta 0:00:01\n",
      "   ------------------------------- -------- 19.1/24.0 MB 14.6 MB/s eta 0:00:01\n",
      "   ------------------------------- -------- 19.1/24.0 MB 14.6 MB/s eta 0:00:01\n",
      "   --------------------------------- ------ 20.3/24.0 MB 13.9 MB/s eta 0:00:01\n",
      "   ---------------------------------- ----- 20.9/24.0 MB 14.2 MB/s eta 0:00:01\n",
      "   ----------------------------------- ---- 21.2/24.0 MB 13.4 MB/s eta 0:00:01\n",
      "   ------------------------------------ --- 21.8/24.0 MB 13.1 MB/s eta 0:00:01\n",
      "   ------------------------------------ --- 22.1/24.0 MB 12.6 MB/s eta 0:00:01\n",
      "   ------------------------------------- -- 22.7/24.0 MB 12.3 MB/s eta 0:00:01\n",
      "   -------------------------------------- - 23.1/24.0 MB 11.9 MB/s eta 0:00:01\n",
      "   ---------------------------------------  23.7/24.0 MB 12.4 MB/s eta 0:00:01\n",
      "   ---------------------------------------  23.9/24.0 MB 11.7 MB/s eta 0:00:01\n",
      "   ---------------------------------------  24.0/24.0 MB 11.5 MB/s eta 0:00:01\n",
      "   ---------------------------------------- 24.0/24.0 MB 10.9 MB/s eta 0:00:00\n",
      "Installing collected packages: gensim\n",
      "  Attempting uninstall: gensim\n",
      "    Found existing installation: gensim 4.3.0\n",
      "    Uninstalling gensim-4.3.0:\n",
      "      Successfully uninstalled gensim-4.3.0\n",
      "Successfully installed gensim-4.3.3\n"
     ]
    }
   ],
   "source": [
    "pip install gensim==4.3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8af3c403",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package                       Version\n",
      "----------------------------- ---------------\n",
      "absl-py                       2.1.0\n",
      "aiobotocore                   2.4.2\n",
      "aiofiles                      22.1.0\n",
      "aiohttp                       3.8.3\n",
      "aioitertools                  0.7.1\n",
      "aiosignal                     1.2.0\n",
      "aiosqlite                     0.18.0\n",
      "alabaster                     0.7.12\n",
      "anaconda-catalogs             0.2.0\n",
      "anaconda-client               1.12.0\n",
      "anaconda-navigator            2.4.2\n",
      "anaconda-project              0.11.1\n",
      "annotated-types               0.7.0\n",
      "anyio                         3.5.0\n",
      "appdirs                       1.4.4\n",
      "apyori                        1.1.2\n",
      "argon2-cffi                   21.3.0\n",
      "argon2-cffi-bindings          21.2.0\n",
      "arrow                         1.2.3\n",
      "astroid                       2.14.2\n",
      "astropy                       5.1\n",
      "asttokens                     2.0.5\n",
      "astunparse                    1.6.3\n",
      "async-timeout                 4.0.2\n",
      "atomicwrites                  1.4.0\n",
      "attrs                         24.2.0\n",
      "Automat                       20.2.0\n",
      "autopep8                      1.6.0\n",
      "Babel                         2.11.0\n",
      "backcall                      0.2.0\n",
      "backports.functools-lru-cache 1.6.4\n",
      "backports.tempfile            1.0\n",
      "backports.weakref             1.0.post1\n",
      "bcrypt                        3.2.0\n",
      "beautifulsoup4                4.12.2\n",
      "binaryornot                   0.4.4\n",
      "black                         0.0\n",
      "bleach                        4.1.0\n",
      "bokeh                         3.2.1\n",
      "boltons                       23.0.0\n",
      "boto3                         1.24.28\n",
      "botocore                      1.27.59\n",
      "Bottleneck                    1.3.5\n",
      "brotlipy                      0.7.0\n",
      "browser-cookie3               0.20.1\n",
      "certifi                       2024.2.2\n",
      "cffi                          1.15.1\n",
      "chardet                       4.0.0\n",
      "charset-normalizer            2.0.4\n",
      "click                         8.0.4\n",
      "cloudpickle                   2.2.1\n",
      "clyent                        1.2.2\n",
      "colorama                      0.4.6\n",
      "colorcet                      3.0.1\n",
      "comm                          0.1.2\n",
      "conda                         23.7.4\n",
      "conda-build                   3.26.0\n",
      "conda-content-trust           0.2.0\n",
      "conda_index                   0.2.3\n",
      "conda-libmamba-solver         23.5.0\n",
      "conda-pack                    0.6.0\n",
      "conda-package-handling        2.2.0\n",
      "conda_package_streaming       0.9.0\n",
      "conda-repo-cli                1.0.41\n",
      "conda-token                   0.4.0\n",
      "conda-verify                  3.4.2\n",
      "constantly                    15.1.0\n",
      "contourpy                     1.0.5\n",
      "cookiecutter                  1.7.3\n",
      "cryptography                  41.0.2\n",
      "cssselect                     1.2.0\n",
      "cvxopt                        1.3.2\n",
      "cycler                        0.11.0\n",
      "cytoolz                       0.12.0\n",
      "daal4py                       2023.1.1\n",
      "dask                          2023.6.0\n",
      "datashader                    0.15.1\n",
      "datashape                     0.5.4\n",
      "debugpy                       1.6.7\n",
      "decorator                     5.1.1\n",
      "defusedxml                    0.7.1\n",
      "diff-match-patch              20200713\n",
      "dill                          0.3.9\n",
      "distributed                   2023.6.0\n",
      "distro                        1.9.0\n",
      "dnspython                     2.7.0\n",
      "docstring-to-markdown         0.11\n",
      "docutils                      0.18.1\n",
      "emoji                         2.14.1\n",
      "entrypoints                   0.4\n",
      "et-xmlfile                    1.1.0\n",
      "executing                     0.8.3\n",
      "fastjsonschema                2.16.2\n",
      "filelock                      3.9.0\n",
      "flake8                        6.0.0\n",
      "Flask                         2.2.2\n",
      "flatbuffers                   24.3.25\n",
      "fonttools                     4.25.0\n",
      "frozenlist                    1.3.3\n",
      "fsspec                        2025.2.0\n",
      "future                        0.18.3\n",
      "gast                          0.6.0\n",
      "gensim                        4.3.3\n",
      "gevent                        24.2.1\n",
      "glob2                         0.7\n",
      "google-pasta                  0.2.0\n",
      "greenlet                      3.1.1\n",
      "grpcio                        1.68.1\n",
      "h11                           0.14.0\n",
      "h5py                          3.12.1\n",
      "HeapDict                      1.0.1\n",
      "holoviews                     1.17.0\n",
      "httpcore                      1.0.7\n",
      "httpx                         0.28.1\n",
      "huggingface-hub               0.29.1\n",
      "hvplot                        0.8.4\n",
      "hyperlink                     21.0.0\n",
      "idna                          3.4\n",
      "imagecodecs                   2021.8.26\n",
      "imageio                       2.26.0\n",
      "imagesize                     1.4.1\n",
      "imbalanced-learn              0.10.1\n",
      "importlib-metadata            6.0.0\n",
      "incremental                   21.3.0\n",
      "inflection                    0.5.1\n",
      "iniconfig                     1.1.1\n",
      "intake                        0.6.8\n",
      "intervaltree                  3.1.0\n",
      "ipykernel                     6.19.2\n",
      "ipython                       8.12.0\n",
      "ipython-genutils              0.2.0\n",
      "ipywidgets                    8.0.4\n",
      "isort                         5.9.3\n",
      "itemadapter                   0.3.0\n",
      "itemloaders                   1.0.4\n",
      "itsdangerous                  2.0.1\n",
      "jaraco.classes                3.2.1\n",
      "jedi                          0.18.1\n",
      "jellyfish                     0.9.0\n",
      "Jinja2                        3.1.2\n",
      "jinja2-time                   0.2.0\n",
      "jiter                         0.8.2\n",
      "jmespath                      0.10.0\n",
      "joblib                        1.2.0\n",
      "json5                         0.9.6\n",
      "jsonpatch                     1.32\n",
      "jsonpointer                   2.1\n",
      "jsonschema                    4.17.3\n",
      "jupyter                       1.0.0\n",
      "jupyter_client                7.4.9\n",
      "jupyter-console               6.6.3\n",
      "jupyter_core                  5.3.0\n",
      "jupyter-events                0.6.3\n",
      "jupyter-server                1.23.4\n",
      "jupyter_server_fileid         0.9.0\n",
      "jupyter_server_ydoc           0.8.0\n",
      "jupyter-ydoc                  0.2.4\n",
      "jupyterlab                    3.6.3\n",
      "jupyterlab-pygments           0.1.2\n",
      "jupyterlab_server             2.22.0\n",
      "jupyterlab-widgets            3.0.5\n",
      "keras                         3.7.0\n",
      "keyring                       23.13.1\n",
      "kiwisolver                    1.4.4\n",
      "lazy_loader                   0.2\n",
      "lazy-object-proxy             1.6.0\n",
      "libarchive-c                  2.9\n",
      "libclang                      18.1.1\n",
      "libmambapy                    1.4.1\n",
      "linkify-it-py                 2.0.0\n",
      "llvmlite                      0.40.0\n",
      "lmdb                          1.4.1\n",
      "locket                        1.0.0\n",
      "lxml                          4.9.2\n",
      "lz4                           4.3.2\n",
      "Markdown                      3.4.1\n",
      "markdown-it-py                2.2.0\n",
      "MarkupSafe                    2.1.1\n",
      "matplotlib                    3.7.1\n",
      "matplotlib-inline             0.1.6\n",
      "mccabe                        0.7.0\n",
      "mdit-py-plugins               0.3.0\n",
      "mdurl                         0.1.0\n",
      "menuinst                      1.4.19\n",
      "mistune                       0.8.4\n",
      "mkl-fft                       1.3.6\n",
      "mkl-random                    1.2.2\n",
      "mkl-service                   2.4.0\n",
      "ml-dtypes                     0.4.1\n",
      "mlxtend                       0.23.1\n",
      "more-itertools                8.12.0\n",
      "mpmath                        1.3.0\n",
      "msgpack                       1.0.3\n",
      "multidict                     6.0.2\n",
      "multipledispatch              0.6.0\n",
      "multiprocess                  0.70.17\n",
      "munkres                       1.1.4\n",
      "mypy-extensions               0.4.3\n",
      "mysql                         0.0.3\n",
      "mysqlclient                   2.2.0\n",
      "namex                         0.0.8\n",
      "navigator-updater             0.4.0\n",
      "nbclassic                     0.5.5\n",
      "nbclient                      0.5.13\n",
      "nbconvert                     6.5.4\n",
      "nbformat                      5.7.0\n",
      "nest-asyncio                  1.5.6\n",
      "networkx                      3.1\n",
      "nltk                          3.8.1\n",
      "notebook                      6.5.4\n",
      "notebook_shim                 0.2.2\n",
      "numba                         0.57.0\n",
      "numexpr                       2.8.4\n",
      "numpy                         1.26.4\n",
      "numpydoc                      1.5.0\n",
      "openai                        1.58.1\n",
      "opencv-python                 3.4.18.65\n",
      "openpyxl                      3.1.5\n",
      "opt_einsum                    3.4.0\n",
      "optree                        0.13.1\n",
      "outcome                       1.3.0.post0\n",
      "packaging                     23.0\n",
      "pandas                        2.2.3\n",
      "pandocfilters                 1.5.0\n",
      "panel                         1.2.1\n",
      "param                         1.13.0\n",
      "paramiko                      2.8.1\n",
      "parsel                        1.6.0\n",
      "parso                         0.8.3\n",
      "partd                         1.2.0\n",
      "pathlib                       1.0.1\n",
      "pathspec                      0.10.3\n",
      "patsy                         0.5.3\n",
      "pep8                          1.7.1\n",
      "pexpect                       4.8.0\n",
      "pickleshare                   0.7.5\n",
      "Pillow                        9.4.0\n",
      "pip                           23.2.1\n",
      "pkginfo                       1.9.6\n",
      "platformdirs                  2.5.2\n",
      "plotly                        5.9.0\n",
      "pluggy                        1.0.0\n",
      "ply                           3.11\n",
      "pooch                         1.4.0\n",
      "poyo                          0.5.0\n",
      "prometheus-client             0.14.1\n",
      "prompt-toolkit                3.0.36\n",
      "Protego                       0.1.16\n",
      "protobuf                      5.29.1\n",
      "psutil                        5.9.0\n",
      "ptyprocess                    0.7.0\n",
      "PuLP                          2.8.0\n",
      "pure-eval                     0.2.2\n",
      "py-cpuinfo                    8.0.0\n",
      "pyarrow                       11.0.0\n",
      "pyasn1                        0.4.8\n",
      "pyasn1-modules                0.2.8\n",
      "pycodestyle                   2.10.0\n",
      "pycosat                       0.6.4\n",
      "pycparser                     2.21\n",
      "pycryptodomex                 3.21.0\n",
      "pyct                          0.5.0\n",
      "pycurl                        7.45.2\n",
      "pydantic                      2.10.4\n",
      "pydantic_core                 2.27.2\n",
      "PyDispatcher                  2.0.5\n",
      "pydocstyle                    6.3.0\n",
      "pyerfa                        2.0.0\n",
      "pyflakes                      3.0.1\n",
      "Pygments                      2.15.1\n",
      "PyJWT                         2.4.0\n",
      "pylint                        2.16.2\n",
      "pylint-venv                   2.3.0\n",
      "pyls-spyder                   0.4.0\n",
      "pymongo                       4.10.1\n",
      "PyMySQL                       1.0.2\n",
      "PyNaCl                        1.5.0\n",
      "pyodbc                        4.0.34\n",
      "pyOpenSSL                     23.2.0\n",
      "pyparsing                     3.0.9\n",
      "PyQt5                         5.15.7\n",
      "PyQt5-sip                     12.11.0\n",
      "PyQtWebEngine                 5.15.4\n",
      "pyquery                       2.0.1\n",
      "pyrsistent                    0.18.0\n",
      "PySocks                       1.7.1\n",
      "pytesseract                   0.3.13\n",
      "pytest                        7.4.0\n",
      "python-dateutil               2.8.2\n",
      "python-dotenv                 1.0.1\n",
      "python-json-logger            2.0.7\n",
      "python-lsp-black              1.2.1\n",
      "python-lsp-jsonrpc            1.0.0\n",
      "python-lsp-server             1.7.2\n",
      "python-slugify                5.0.2\n",
      "python-snappy                 0.6.1\n",
      "pytoolconfig                  1.2.5\n",
      "pytz                          2022.7\n",
      "pyviz-comms                   2.3.0\n",
      "PyWavelets                    1.4.1\n",
      "pywin32                       305.1\n",
      "pywin32-ctypes                0.2.0\n",
      "pywinpty                      2.0.10\n",
      "PyYAML                        6.0\n",
      "pyzmq                         23.2.0\n",
      "QDarkStyle                    3.0.2\n",
      "qstylizer                     0.2.2\n",
      "QtAwesome                     1.2.2\n",
      "qtconsole                     5.4.2\n",
      "QtPy                          2.2.0\n",
      "queuelib                      1.5.0\n",
      "regex                         2022.7.9\n",
      "requests                      2.31.0\n",
      "requests-file                 1.5.1\n",
      "requests-toolbelt             1.0.0\n",
      "retrying                      1.3.4\n",
      "rfc3339-validator             0.1.4\n",
      "rfc3986-validator             0.1.1\n",
      "rich                          13.9.4\n",
      "rope                          1.7.0\n",
      "Rtree                         1.0.1\n",
      "ruamel.yaml                   0.17.21\n",
      "ruamel-yaml-conda             0.17.21\n",
      "s3fs                          2023.3.0\n",
      "s3transfer                    0.6.0\n",
      "sacremoses                    0.0.43\n",
      "safetensors                   0.5.2\n",
      "SALib                         1.5.1\n",
      "scikit-image                  0.20.0\n",
      "scikit-learn                  1.4.2\n",
      "scikit-learn-intelex          20230426.121932\n",
      "scipy                         1.13.1\n",
      "Scrapy                        2.8.0\n",
      "seaborn                       0.12.2\n",
      "selenium                      4.5.0\n",
      "Send2Trash                    1.8.0\n",
      "service-identity              18.1.0\n",
      "setuptools                    68.0.0\n",
      "shadowcopy                    0.0.4\n",
      "sip                           6.6.2\n",
      "six                           1.16.0\n",
      "smart-open                    5.2.1\n",
      "sniffio                       1.3.1\n",
      "snowballstemmer               2.2.0\n",
      "sortedcontainers              2.4.0\n",
      "soupsieve                     2.4\n",
      "Sphinx                        5.0.2\n",
      "sphinxcontrib-applehelp       1.0.2\n",
      "sphinxcontrib-devhelp         1.0.2\n",
      "sphinxcontrib-htmlhelp        2.0.0\n",
      "sphinxcontrib-jsmath          1.0.1\n",
      "sphinxcontrib-qthelp          1.0.3\n",
      "sphinxcontrib-serializinghtml 1.1.5\n",
      "spyder                        5.4.3\n",
      "spyder-kernels                2.4.3\n",
      "SQLAlchemy                    1.4.39\n",
      "stack-data                    0.2.0\n",
      "stanza                        1.10.1\n",
      "statsmodels                   0.14.0\n",
      "supar                         1.1.4\n",
      "sympy                         1.11.1\n",
      "tables                        3.8.0\n",
      "tabulate                      0.8.10\n",
      "TBB                           0.2\n",
      "tblib                         1.7.0\n",
      "tenacity                      8.2.2\n",
      "tensorboard                   2.18.0\n",
      "tensorboard-data-server       0.7.2\n",
      "tensorflow                    2.18.0\n",
      "tensorflow_intel              2.18.0\n",
      "tensorflow-io-gcs-filesystem  0.31.0\n",
      "termcolor                     2.5.0\n",
      "terminado                     0.17.1\n",
      "tesserocr                     2.7.1\n",
      "text-unidecode                1.3\n",
      "textdistance                  4.2.1\n",
      "threadpoolctl                 2.2.0\n",
      "three-merge                   0.1.1\n",
      "tifffile                      2021.7.2\n",
      "tinycss2                      1.2.1\n",
      "tldextract                    3.2.0\n",
      "tokenizers                    0.21.0\n",
      "toml                          0.10.2\n",
      "tomlkit                       0.11.1\n",
      "toolz                         0.12.0\n",
      "torch                         2.2.2\n",
      "tornado                       6.3.2\n",
      "tqdm                          4.65.0\n",
      "traitlets                     5.7.1\n",
      "transformers                  4.49.0\n",
      "trio                          0.26.2\n",
      "trio-websocket                0.11.1\n",
      "Twisted                       22.10.0\n",
      "twisted-iocpsupport           1.0.2\n",
      "typing_extensions             4.12.2\n",
      "tzdata                        2024.2\n",
      "uc-micro-py                   1.0.1\n",
      "ujson                         5.4.0\n",
      "Unidecode                     1.2.0\n",
      "urllib3                       1.26.16\n",
      "urllib3-secure-extra          0.1.0\n",
      "w3lib                         1.21.0\n",
      "watchdog                      2.1.6\n",
      "wcwidth                       0.2.5\n",
      "webdriver-manager             4.0.2\n",
      "webencodings                  0.5.1\n",
      "websocket-client              1.8.0\n",
      "Werkzeug                      2.2.3\n",
      "whatthepatch                  1.0.2\n",
      "wheel                         0.38.4\n",
      "widgetsnbextension            4.0.5\n",
      "win-inet-pton                 1.1.0\n",
      "WMI                           1.5.1\n",
      "wordcloud                     1.9.4\n",
      "wrapt                         1.14.1\n",
      "wsproto                       1.2.0\n",
      "xarray                        2023.6.0\n",
      "xgboost                       2.1.2\n",
      "xlrd                          2.0.1\n",
      "xlwings                       0.29.1\n",
      "xlwt                          1.3.0\n",
      "xyzservices                   2022.9.0\n",
      "y-py                          0.5.9\n",
      "yapf                          0.31.0\n",
      "yarl                          1.8.1\n",
      "ypy-websocket                 0.8.2\n",
      "zict                          2.2.0\n",
      "zipp                          3.11.0\n",
      "zope.event                    5.0\n",
      "zope.interface                5.4.0\n",
      "zstandard                     0.19.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c242d6be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting blosc2~=2.0.0\n",
      "  Obtaining dependency information for blosc2~=2.0.0 from https://files.pythonhosted.org/packages/03/d0/685258d475e09d8884d64c8a1eb90a3428fbba782e60b7926f9fdb82c297/blosc2-2.0.0-cp311-cp311-win_amd64.whl.metadata\n",
      "  Downloading blosc2-2.0.0-cp311-cp311-win_amd64.whl.metadata (11 kB)\n",
      "Requirement already satisfied: msgpack in c:\\users\\31873\\anaconda3\\lib\\site-packages (from blosc2~=2.0.0) (1.0.3)\n",
      "Downloading blosc2-2.0.0-cp311-cp311-win_amd64.whl (1.9 MB)\n",
      "   ---------------------------------------- 0.0/1.9 MB ? eta -:--:--\n",
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      "   ---------------------------------------- 1.9/1.9 MB 3.4 MB/s eta 0:00:00\n",
      "Installing collected packages: blosc2\n",
      "Successfully installed blosc2-2.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install blosc2~=2.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bacd979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Cython in c:\\users\\31873\\anaconda3\\lib\\site-packages (3.0.12)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Cython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67c1e6b1",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "下面的例子将展示词向量标准工具包——gensim提供的词嵌入，并展示词嵌入如何表示词的相似度。\n",
    "<!-- https://nlp.stanford.edu/projects/glove/ -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5c5a740a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pprint\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "\n",
    "# 从GloVe官网下载GloVe向量，此处使用的是glove.6B.zip\n",
    "# 解压缩zip文件并将以下路径改为解压后对应文件的路径\n",
    "model = KeyedVectors.load_word2vec_format('D:/glove.6B'+\\\n",
    "    '/glove.6B.100d.txt', binary=False, no_header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01a2e4a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('movie', 0.9055121541023254),\n",
      " ('films', 0.8914433717727661),\n",
      " ('directed', 0.8124362826347351),\n",
      " ('documentary', 0.8075793981552124),\n",
      " ('drama', 0.7929168939590454),\n",
      " ('movies', 0.7889865040779114),\n",
      " ('comedy', 0.7842751145362854),\n",
      " ('starring', 0.7573285102844238),\n",
      " ('cinema', 0.7419455647468567),\n",
      " ('hollywood', 0.7307389974594116)]\n",
      "[('vehicle', 0.8630837798118591),\n",
      " ('truck', 0.8597878813743591),\n",
      " ('cars', 0.837166965007782),\n",
      " ('driver', 0.8185911178588867),\n",
      " ('driving', 0.781263530254364),\n",
      " ('motorcycle', 0.7553156614303589),\n",
      " ('vehicles', 0.7462257146835327),\n",
      " ('parked', 0.74594646692276),\n",
      " ('bus', 0.737270712852478),\n",
      " ('taxi', 0.7155269384384155)]\n"
     ]
    }
   ],
   "source": [
    "# 使用most_similar()找到词表中距离给定词最近（最相似）的n个词\n",
    "pprint.pprint(model.most_similar('film'))\n",
    "pprint.pprint(model.most_similar('car'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b62f7ad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "japanese\n",
      "panda\n",
      "longest\n",
      "terrible\n",
      "queen\n"
     ]
    }
   ],
   "source": [
    "# 利用GloVe展示一个类比的例子\n",
    "def analogy(x1, x2, y1):\n",
    "    # 寻找top-N最相似的词。\n",
    "    result = model.most_similar(positive=[y1, x2], negative=[x1])\n",
    "    return result[0][0]\n",
    "\n",
    "print(analogy('china', 'chinese', 'japan'))\n",
    "print(analogy('australia', 'koala', 'china'))\n",
    "print(analogy('tall', 'tallest', 'long'))\n",
    "print(analogy('good', 'fantastic', 'bad'))\n",
    "print(analogy('man', 'woman', 'king'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c308cee",
   "metadata": {},
   "source": [
    "下面将展示word2vec的代码，包括文本预处理、skipgram算法的实现、以及使用PyTorch进行优化。这里使用《小王子》这本书作为训练语料。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "590fc408",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装NLTK，使用如下代码下载punkt组件\n",
    "#import nltk\n",
    "#nltk.download('punkt')\n",
    "\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from collections import defaultdict\n",
    "\n",
    "# 使用类管理数据对象，包括文本读取、文本预处理等\n",
    "class TheLittlePrinceDataset:\n",
    "    def __init__(self, tokenize=True):\n",
    "        # 利用NLTK函数进行分句和分词\n",
    "        text = open('the little prince.txt', 'r', encoding='utf-8').read()\n",
    "        if tokenize:\n",
    "            self.sentences = sent_tokenize(text.lower())\n",
    "            self.tokens = [word_tokenize(sent) for sent in self.sentences]\n",
    "        else:\n",
    "            self.text = text\n",
    "\n",
    "    def build_vocab(self, min_freq=1):\n",
    "        # 统计词频\n",
    "        frequency = defaultdict(int)\n",
    "        for sentence in self.tokens:\n",
    "            for token in sentence:\n",
    "                frequency[token] += 1\n",
    "        self.frequency = frequency\n",
    "\n",
    "        # 加入<unk>处理未登录词，加入<pad>用于对齐变长输入进而加速\n",
    "        self.token2id = {'<unk>': 1, '<pad>': 0}\n",
    "        self.id2token = {1: '<unk>', 0: '<pad>'}\n",
    "        for token, freq in sorted(frequency.items(), key=lambda x: -x[1]):\n",
    "            # 丢弃低频词\n",
    "            if freq > min_freq:\n",
    "                self.token2id[token] = len(self.token2id)\n",
    "                self.id2token[len(self.id2token)] = token\n",
    "            else:\n",
    "                break\n",
    "\n",
    "    def get_word_distribution(self):\n",
    "        distribution = np.zeros(vocab_size)\n",
    "        for token, freq in self.frequency.items():\n",
    "            if token in dataset.token2id:\n",
    "                distribution[dataset.token2id[token]] = freq\n",
    "            else:\n",
    "                # 不在词表中的词按<unk>计算\n",
    "                distribution[1] += freq\n",
    "        distribution /= distribution.sum()\n",
    "        return distribution\n",
    "\n",
    "    # 将分词结果转化为索引表示\n",
    "    def convert_tokens_to_ids(self, drop_single_word=True):\n",
    "        self.token_ids = []\n",
    "        for sentence in self.tokens:\n",
    "            token_ids = [self.token2id.get(token, 1) for token in sentence]\n",
    "            # 忽略只有一个token的序列，无法计算loss\n",
    "            if len(token_ids) == 1 and drop_single_word:\n",
    "                continue\n",
    "            self.token_ids.append(token_ids)\n",
    "        \n",
    "        return self.token_ids\n",
    "\n",
    "dataset = TheLittlePrinceDataset()\n",
    "dataset.build_vocab(min_freq=1)\n",
    "sentences = dataset.convert_tokens_to_ids()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "efc882de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(76044, 2) [[  4  16]\n",
      " [  4  19]\n",
      " [ 16   4]\n",
      " ...\n",
      " [130   3]\n",
      " [  3  86]\n",
      " [  3 130]]\n"
     ]
    }
   ],
   "source": [
    "# 遍历所有的中心词-上下文词对\n",
    "window_size = 2\n",
    "data = []\n",
    "\n",
    "for sentence in sentences:\n",
    "    for i in range(len(sentence)):\n",
    "        for j in range(i-window_size, i+window_size+1):\n",
    "            if j == i or j < 0 or j >= len(sentence):\n",
    "                continue\n",
    "            center_word = sentence[i]\n",
    "            context_word = sentence[j]\n",
    "            data.append([center_word, context_word])\n",
    "\n",
    "# 需要提前安装numpy\n",
    "import numpy as np\n",
    "data = np.array(data)\n",
    "print(data.shape, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "30903b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要提前安装PyTorch\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# 实现skipgram算法，使用对比学习计算损失\n",
    "class SkipGramNCE(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_size, distribution,\\\n",
    "                 neg_samples=20):\n",
    "        super(SkipGramNCE, self).__init__()\n",
    "        print(f'vocab_size = {vocab_size}, embed_size = {embed_size}, '+\\\n",
    "              f'neg_samples = {neg_samples}')\n",
    "        self.input_embeddings = nn.Embedding(vocab_size, embed_size)\n",
    "        self.output_embeddings = nn.Embedding(vocab_size, embed_size)\n",
    "        distribution = np.power(distribution, 0.75)\n",
    "        distribution /= distribution.sum()\n",
    "        self.distribution = torch.tensor(distribution)\n",
    "        self.neg_samples = neg_samples\n",
    "        \n",
    "    def forward(self, input_ids, labels):\n",
    "        i_embed = self.input_embeddings(input_ids)\n",
    "        o_embed = self.output_embeddings(labels)\n",
    "        batch_size = i_embed.size(0)\n",
    "        n_words = torch.multinomial(self.distribution, batch_size * \\\n",
    "            self.neg_samples, replacement=True).view(batch_size, -1)\n",
    "        n_embed = self.output_embeddings(n_words)\n",
    "        pos_term = F.logsigmoid(torch.sum(i_embed * o_embed, dim=1))\n",
    "        # 负采样，用于对比学习\n",
    "        neg_term = F.logsigmoid(- torch.bmm(n_embed, \\\n",
    "            i_embed.unsqueeze(2)).squeeze())\n",
    "        neg_term = torch.sum(neg_term, dim=1)\n",
    "        loss = - torch.mean(pos_term + neg_term)\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d9da6c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 5.43983724e-02 5.34295679e-02 ... 9.68804495e-05\n",
      " 9.68804495e-05 9.68804495e-05]\n",
      "vocab_size = 1078, embed_size = 128, neg_samples = 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "epoch-99, loss=3.0925: 100%|█| 100/100 [05:53<00:00,  3.53s/\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为对比学习负采样准备词频率分布\n",
    "vocab_size = len(dataset.token2id)\n",
    "embed_size = 128\n",
    "distribution = dataset.get_word_distribution()\n",
    "print(distribution)\n",
    "model = SkipGramNCE(vocab_size, embed_size, distribution)\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.optim import SGD, Adam\n",
    "\n",
    "# 定义静态方法collate_batch批量处理数据，转化为PyTorch可以需要的张量类型\n",
    "class DataCollator:\n",
    "    @classmethod\n",
    "    def collate_batch(cls, batch):\n",
    "        batch = np.array(batch)\n",
    "        input_ids = torch.tensor(batch[:, 0], dtype=torch.long)\n",
    "        labels = torch.tensor(batch[:, 1], dtype=torch.long)\n",
    "        return {'input_ids': input_ids, 'labels': labels}\n",
    "\n",
    "# 定义训练参数以及训练循环\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "learning_rate = 1e-3\n",
    "epoch_loss = []\n",
    "\n",
    "data_collator = DataCollator()\n",
    "dataloader = DataLoader(data, batch_size=batch_size, shuffle=True,\\\n",
    "    collate_fn=data_collator.collate_batch)\n",
    "optimizer = Adam(model.parameters(), lr=learning_rate)\n",
    "model.zero_grad()\n",
    "model.train()\n",
    "\n",
    "# 需要提前安装tqdm\n",
    "from tqdm import trange\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 训练过程，每步读取数据，送入模型计算损失，并使用PyTorch进行优化\n",
    "with trange(epochs, desc='epoch', ncols=60) as pbar:\n",
    "    for epoch in pbar:\n",
    "        for step, batch in enumerate(dataloader):\n",
    "            loss = model(**batch)\n",
    "            pbar.set_description(f'epoch-{epoch}, loss={loss.item():.4f}')\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            model.zero_grad()\n",
    "        epoch_loss.append(loss.item())\n",
    "    \n",
    "epoch_loss = np.array(epoch_loss)\n",
    "plt.plot(range(len(epoch_loss)), epoch_loss)\n",
    "plt.xlabel('training epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9430e9a",
   "metadata": {},
   "source": [
    "TF-IDF加权\n",
    "\n",
    "定义词频率（term frequency）。注意到不同长度的文章词频率会有较大差距，不利于比较和运算，因此可以对词频率取对数。\n",
    "\n",
    "$$\\text{tf}_{t,d} = \\log (\\text{count}(t,d) + 1)$$\n",
    "\n",
    "其中$\\text{count}(t,d)$表示词$t$在文档$d$中出现的次数，为了避免对0取对数，把所有的计数加1。\n",
    "\n",
    "那么如何区分高频词与低频词呢？TF-IDF引入了另一个重要的评价指标——文档频率（document frequency），即一个词在语料库所包含的多少篇文档中出现。在所有文档里出现的词往往是虚词或是常见实词，而只在少量文档里出现的词往往是具有明确含义的实词并且具有很强的文档区分度。用$\\text{df}_t$来表示在多少篇文档中出现了词$t$。\n",
    "\n",
    "为了压低高频词和提升低频词的影响，TF-IDF使用文档频率的倒数，也就是逆向文档频率（inverse document frequency）来对词频率进行加权。这很好理解，一个词的文档频率越高，其倒数就越小，权重就越小。\n",
    "\n",
    "$$\\text{idf}_t = \\log \\frac{N}{\\text{df}_t}$$\n",
    "\n",
    "其中$N$表示文档总数。为了避免分母为0，通常会将分母改为$\\text{df}_t+1$。\n",
    "\n",
    "基于词频率和逆向文档频率，得到TF-IDF的最终值为：\n",
    "\n",
    "$$w_{t,d} = \\text{tf}_{t,d} \\times \\text{idf}_{t}$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f765e353",
   "metadata": {},
   "source": [
    "很多情况下会额外对文档的TF-IDF向量使用L2归一化，使得不同文档的TF-IDF向量具有相同的模长，便于相互比较。\n",
    "下面给出了TF-IDF的代码实现。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ce8e610",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TFIDF:\n",
    "    def __init__(self, vocab_size, norm='l2', smooth_idf=True,\\\n",
    "                 sublinear_tf=True):\n",
    "        self.vocab_size = vocab_size\n",
    "        self.norm = norm\n",
    "        self.smooth_idf = smooth_idf\n",
    "        self.sublinear_tf = sublinear_tf\n",
    "    \n",
    "    def fit(self, X):\n",
    "        doc_freq = np.zeros(self.vocab_size, dtype=np.float64)\n",
    "        for data in X:\n",
    "            for token_id in set(data):\n",
    "                doc_freq[token_id] += 1\n",
    "        doc_freq += int(self.smooth_idf)\n",
    "        n_samples = len(X) + int(self.smooth_idf)\n",
    "        self.idf = np.log(n_samples / doc_freq) + 1\n",
    "    \n",
    "    def transform(self, X):\n",
    "        assert hasattr(self, 'idf')\n",
    "        term_freq = np.zeros((len(X), self.vocab_size), dtype=np.float64)\n",
    "        for i, data in enumerate(X):\n",
    "            for token in data:\n",
    "                term_freq[i, token] += 1\n",
    "        if self.sublinear_tf:\n",
    "            term_freq = np.log(term_freq + 1)\n",
    "        Y = term_freq * self.idf\n",
    "        if self.norm:\n",
    "            row_norm = (Y**2).sum(axis=1)\n",
    "            row_norm[row_norm == 0] = 1\n",
    "            Y /= np.sqrt(row_norm)[:, None]\n",
    "        return Y\n",
    "    \n",
    "    def fit_transform(self, X):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "706d4e69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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